Influence Water Quality Research Articles

Regional Drivers of Stream Chemical Behavior: Leveraging Lithology, Land Use, and Climate Gradients Across the Colorado River, Texas USA

AbstractUnderstanding relationships between stream chemistry and watershed factors: land use/land cover, climate, and lithology are crucial to improving our knowledge of critical zone processes that influence water quality. We compiled major ion data from >100 monitoring stations collected over 60 years (1958–2018) across the Colorado River Watershed in Texas (103,000 km2). We paired this river chemistry data with complementary lithology, land use, climate, and stream discharge information. Machine learning techniques were used to produce new insights on controls of stream water chemical behavior, which were validated using traditional multivariate analyses. Studies on stream flow and chemistry in the American west and globally have shown strong relationships between major ion chemical composition, climate, and lithology which hold true for the Colorado River basin in this study. Reactive minerals, including carbonates and evaporites, dominate major ion chemistry across the upper, low‐precipitation regions of the watershed. Upstream and middle reaches of the Colorado River showed shifts from Na‐Cl‐SO4 dominated water from multiple sources including dissolution of gypsum and halite in shallow groundwater, and agricultural activities, to Ca‐HCO3 water types controlled by carbonate dissolution. In the lower portion of the watershed multiple analyses demonstrate that stream chemistry is more influenced by greater precipitation and the presence of silicate minerals than the middle and upstream reaches. This study demonstrates the power of applying machine learning approaches to publicly available long term water chemistry data sets to improve the understanding of watershed interactions with surficial lithology, salinity sources, and anthropogenic influences of water quality.

Comparison of anthracite and GAC biofilter performance for surface‐water manganese removal

AbstractAlameda County Water District has observed increased biofilter effluent manganese concentrations during winter operations. To investigate manganese removal across surface water biofilters during cold‐water conditions, trends were analyzed between water temperature and manganese removal across multiple testing scales, multiple biofilter influent water qualities, and both anthracite and GAC media. During acclimation of new biofilters, 100% removal of manganese was observed sooner across both anthracite and GAC biofilters brought online at 20°C compared to 12°C. Acclimation at 12°C required 18 extra days for the GAC biofilters and 48 additional days for the anthracite biofilters. For fully acclimated biofilters, a decrease in manganese removal across both GAC and anthracite biofilters at temperatures below 15°C was observed. However, greater and more consistent manganese removal was observed across GAC compared to anthracite biofilters. Performance differences between locations also suggest that operational and water quality conditions also likely affect manganese removal.

Influence of Hydraulic Model Complexity on Results of Water Age and Quality Simulation in Municipal Water Supply Systems

The age of water in the municipal water supply system is one of the main factors influencing water quality. To create a good quality hydraulic model, one must achieve a high level of calibration accuracy with real life measurement data. Before we start building our model, we must decide on the model’s level of detail, that is, its complexity. We must know if skeletonization of the network graph and different hydraulic timesteps have an influence on simulation results. This study strives to prove that this decision can lead to unforeseen problems during the calibration process, thus making it impossible to achieve the required calibration precision. In order to prove this, two different model variants were created with different levels of graph detail, and simulation data results were used to determine which model variant is best suited to achieve the highest fidelity simulation results. Following this, the chosen model was run with different hydraulic timestep settings, which made it possible to showcase the large influence this setting has on achieved results.

Evaluation of impacts of environmental factors and land use on seasonal surface water quality in arid and humid regions using structural equation models

Understanding of the buffer scale effect on water quality changes analysis in arid and humid regions is of great significance for exploring the best scale for water quality pollution and scientific planning of land use. The objective of this study is to determine the optimal buffer scales (500, 1000, 1500, 2000, 2500 and 3000 m) of land use that affect ammonia nitrogen (NH3-N) and total phosphorus (TP) in the Jing-Bortala River Watershed (JBRW) (arid region) and the Liyang Region (LR) (humid region) under a site buffer. PLS-SEM was used to fit the pathway relationships between the influencing factors and water quality to investigate the driving factors of NH3-N and TP in different seasons. The results showed that: (1) seasonal fluctuations in NH3-N and TP concentrations were found in both the arid and humid regions, with the arid region having higher values than humid region; the coefficient of variation (CV) of NH3-N and TP was higher in arid and humid region, and the CV of NH3-N and TP were higher in spring and autumn in arid region than that in spring and autumn in humid region; (2) the 2500 m (0.262 ≤ R2 ≤ 0.633) buffer is the optimal scale for studying NH3-N and TP in arid region, whereas the 2000 m (0.262 ≤ R2 ≤ 0.686) scale works well in humid region; and (3) farmland (NDVI) was the largest influencing factor for NH3-N and TP changes in arid region, accounting for 28.95 % of the average percent total impacts. Furthermore, water area (NDWI) was the predominant influencing factor in humid region, occupying 33.93 % of the average percent total impacts. The influence of land use type (indices) on surface water quality is greater than that of environmental factors. Thus, these new insights into an appropriate buffer scale can be used as a guide for water pollution management in arid and humid environments.

Role of Macrophytes: A Review

Macrophytes are large aquatic plants belonging to taxonomically diverse groups. They are adapted morphologically, anatomically and physiologically to grow in aquatic habitats of fresh water, brackish water or sea water. This review is based on the rigorous study of literature and papers based on work done on wetlands and macrophytes. Surveys show that macrophytes form an important component in the structure and functioning of an aquatic ecosystem. They play a vital role in the sustenance of life by providing habitat, food, fodder, fiber, green manure, medicines and other useful products. They act as a bioindicator, influence water quality, perform nutrient cycling and reduce shore erosion. The work concluded that knowledge of floral diversity in any aquatic habitat is essential as each and every plant has its role in food chain and they must be documented and conserved before they become extinct without being acknowledged. Researches must be undertaken to scientifically study and extract the important constituents from unknown flora by identifying them with the help of indigenous people so that it can be further flourished for the welfare of mankind. The aim of this paper is to review the diverse roles of macrophytes and to focus on the conservation of aquatic resources and the biodiversity within them, as they not only provide various ecosystem services but are important for the livelihood and ecological balance.

The potential role of sediments in nutrients (N, P) cycle in Marchica lagoon (Mediterranean Sea, Morocco)

The aim of this study is to present the distribution of nutrients in water and sediment of Marchica lagoon, a post-restorated lagoon located in Mediterranean Sea (NE Morocco), to estimate the impact of bottom sediments on the water quality of this lagoon, and to identify the areas more affected by eutrophication risk. Nutrient concentrations in the water column and sediments were determined in thirteen samples taken from different station of the lagoon in October 2019. Nutrient concentrations in sediment were found higher than those in surface water; very high levels of nitrogenous elements showed in all sediments samples and mainly nitrogenous components (averages of 43.58 mg of N-NH4; 0.827 mg of N-NO3; 0.054 mg of N-NO2 and 773 mg of TN per kg of dry sediment) and high levels of phosphorus with average of 620 mg P/kg of dry sediment. A strong positive correlation showed between sediment and water parameters in 0.01 and 0.05 levels. High concentrations were recorded near the nutrient-rich wastewater treatment plant effluent discharges, watershed and groundwater input. The lowest concentrations are near the new pass, this is due to the low continental inputs and the renewal of the lagoon waters by the Mediterranean Sea. Generally, the distribution of nutrient levels is influenced by continental inputs, hydrodynamics, and organic matter content in the sediment. These sediments are considered as a reservoir of nutrient pollutants, which will influence water quality by releasing sedimentary materials in the water.

Spatiotemporal dynamics and anthropologically dominated drivers of chlorophyll-a, TN and TP concentrations in the Pearl River Estuary based on retrieval algorithm and random forest regression

Estimation of large-scale and high-precision water quality parameters is critical in explaining the spatiotemporal dynamics and the driving factors of water quality variability, especially in areas with environmental complexity (e.g., crisscrossing waterways, high flood risk in rainy season and seawater invasion). Thus, in this study, a retrieval algorithm was developed to predict chlorophyll-a (Chl-a), total nitrogen (TN) and total phosphorus (TP) concentrations in the Pearl River Estuary (PRE) based on a large amount of in situ measurements and Landsat 8 remote sensing images. Random Forest (RF) machine learning was conducted to identify the relationship between environmental indicators (pH, turbidity, conductivity, total dissolved solids and water temperature), Chl-a, TN and TP. The results showed that the NIR/R Binomial algorithm for Chl-a estimation presented appreciable reliability with R2 of 0.7429, root mean square error (RMSE) of 1.2089 and mean absolute percent error (MAPE) of 15.33%. The water quality variation in the PRE showed a characteristic of overall improvement and regional deterioration with average concentrations of 7.28 μg/L, 1.15 mg/L and 0.12 mg/L for Chl-a, TN, and TP respectively. Turbidity and pH were identified as the most important indicators to explain Chl-a (52.86%, 39.91%), TN (52.38%, 40.57%) and TP (55.23%, 40.03%) variation. Agricultural pollution was the main pollution source due to the intensive application of fertilizer and increased field size. Besides, land use patterns (e.g., increasing farmland but decreasing forest) greatly influenced water quality from 2010 to 2020. Moreover, light limitation caused by high turbidity reduced the algae productivity and further lowered the Chl-a concentration. The driving factors for regional water quality variations were anthropologically dominated and supplemented by climate change. This study improved the monitoring accuracy of regional water environment and provided quantitative early warning of water pollution events for environmental practitioners, so as to achieve long-term monitoring, precise pollution management and efficient water resources management.

Land-Use Pattern as a Key Factor Determining the Water Quality, Fish Guilds, and Ecological Health in Lotic Ecosystems of the Asian Monsoon Region

Land-use patterns influence water quality in lotic ecosystems worldwide; consequently, deteriorating water quality affects fish communities and composition and the ecological health of water bodies. This study aimed to evaluate how land use, stream order, and elevation regulate water quality and ecological health in 64 streams based on the following four land cover types: namely, forest, agriculture, urban upstream, and urban downstream regions. Spatial analysis revealed that urban downstream areas had higher nutrient concentrations [total phosphorus (TP) as follows: 117 µg/L; total nitrogen (TN): 5.57 mg/L] and organic pollutants [chemical oxygen demand (COD): 7.71] than other regions. Empirical analysis indicated that TP (R2 = 0.46) had a high relation with chlorophyll-a (Chl-a) compared to TN (R2 = 0.23) and TN:TP (R2 = 0.20). Elevation, stream order, and monsoon season significantly impact nutrients, organic matter, suspended particles, ionic content, and algal chlorophyll concentrations. The index of biotic integrity (IBI) was significantly positively correlated with elevation (R2 = 0.387), indicating that forest streams (high elevation) had better water quality and ecological health than lower-elevation streams. The proportion of insectivore species shows a significant negative relationship with biological oxygen demand (BOD) (R2 = 0.123) and TP (R2 = 0.155). The multi-metric index of biotic integrity (IBI) model suggested that the ecological health of forest streams was in fair condition. In contrast, agricultural streams were in poor condition, and urban upstream and downstream were in very poor conditions. The outcomes of this study indicated that land-use patterns and elevation largely regulate the water quality and ecological health of the streams.

Land use, hydrology, and climate influence water quality of China's largest river

Influences of multiple environmental factors on water quality patterns is less studied in large rivers. Landscape analysis, multiple statistical methods, and the water quality index (WQI) were used to detect water quality patterns and influencing factors in China's largest river, the Yangtze River. Compared with the dry season, the wet season had significantly higher total phosphorus (TP), chemical oxygen demand (COD), total suspended solids (TSS), and turbidity (TUR). The WQI indicated "Moderate" and "Good" water quality in the wet and dry seasons, respectively. Compared with other sites, the upper reach sites that immediately downstream of the Three Gorges Dam had lower TP, TN, TSS and TUR in both seasons, and had lower and higher water temperature in the wet and dry seasons, respectively. Water quality patterns were mainly driven by heterogeneity in land use (i.e., wetland, cropland, and urban land), hydrology (i.e., water flow, water level), and climate (i.e., rainfall, air temperature). Water quality in the wet season was primarily driven by land use while the joint effect of land use and hydrology primarily drove in the dry season. Decision-makers and regulators of large river basin management may need to develop programs that consider influences from both human and natural drivers for water quality conservation.

Effect of multiple spatial scale characterization of land use on water quality.

Land use in uplands is an important factor affecting water quality in its respective catchment, and its influences at the different spatial scales and configurations warrant further investigation. Here, we selected 26 catchments in the upper Han River (China) and sampled the surface water at the outlet of each catchment in four seasons during 2019. Multivariate statistics were used to identify the relationships between land use characteristics in uplands and water quality in river system. The results indicated that chemical oxygen demand (CODMn); pH; dissolved oxygen; electrical conductivity; nutrient, i.e., NH4+-N, NO3--N; and dissolved phosphorus (DP) in rivers displayed significant seasonal variations. Stepwise regression revealed that landscape metrics such as patch density, landscape shape index, and splitting index were important factors influencing water quality in rivers regardless of their spatiality and seasonality. Urban was the most frequently chosen land-use type in the best prediction models, and forest area showed a negative correlation with water quality parameters in most cases for example, DP. Overall, the influence of land use on river water quality was slightly stronger at reach scale than at catchment and riparian scales. Also, nutrients (i.e., NH4+-N, NO3--N, and DP) in rivers were primarily impacted by the land use characteristic at catchment and riparian scales. Our results suggested that multi-scale explorations would help to achieve a fully understanding on the impacts of land use on river water quality.

Improved neural network with least square support vector machine for wastewater treatment process

This research offers a unique interval by using the predicting approach for discharge indicators of water quality data such as biochemical oxygen demand (BOD) and ammonia nitrogen (NH3–N). This is considered one of the significant quality metrics in wastewater treatment plants for water quality management as well as surveillance. To begin, the effluent information for BOD/NH3–N and their supplementary parameters are gathered. Hence BOD and NH3 are considered major feature sources for estimating water pollutants. BOD is high then oxygen level is very low in the water due to pollutants or algae. Ammonia nitrogen is an organic waste component in water from sewage. The significant characteristics with good correlation levels of BOD and NH3–N are examined and identified using a grey correlation analysis method after certain basic data pre-processing procedures. The BOD/NH3–N effluent information of a water treatment plant is predicted using an upgraded feed-forward neural network with the least square support vector machine (FFNN-LSSVM) method. An optimization approach for an enhanced feed-forward neural network (IFFNN) is built by Machine Learning Algorithms. The IFFNN used regular influent water quality, influent rate of flow, and Wastewater performance monitoring and operational conditions as input parameters. For future prediction, input variables were previous different wastewater quality measurements. Lastly, the analysis shows that, when compared to other current algorithms, the proposed methodology can forecast wastewater quality of water with high accuracy in predicting BOD and NH3 levels, limited computation duration, mean error less than 10% and R2 is 90% proves better than existing techniques.

A suitable deterministic modeling approach of urban stormwater nutrient, metal and organic pollutant removal in plant biofiltration

ABSTRACT Urban water managers regard stormwater runoff and its treatment as one of the greatest challenges, due to its diffused nature and pollutant range. In response, plant biofiltration, a water-sensitive urban design technology, is increasingly preferred. However, due to varying removal performances influenced by design and operational conditions, as well as pollutant removal process complexity, it is under-utilised. This note reviews relevant pollutant removal processes and pathways; and subsequently proffers a first step summary of fundamental processes and pathways applicable to generic plant biofiltration for removal of typical stormwater pollutants. The treatment model combines relevant existing models applicable to the removal pathways of organics, NH3, NO3-, PO4 3- and metal pollutant change, as well as recommends further refinements. Advancing the understanding of plant biofilter complexity represents a significant in the urban drainage management strategy, from simple flood reduction to the recognition of the stochastic nature of interactions influencing water quality.

A review of wildfire impacts on stream temperature and turbidity across scales

Wildfire has increased in severity and frequency with climate change and human activities in recent years, threatening water-related ecosystem services. Forested watersheds are at risk of impacts of wildfires that alter land cover, and hydrological processes, and influence drinking water quality and aquatic habitat. To date, most research on post-fire hydrologic effects has focused on water quantity, while stream temperature and turbidity received less attention. In this study, we reviewed 62 articles to examine wildfire drivers and processes associated with turbidity and stream temperature behavior through a geographic lens in the context of ecosystem services. Our goals were to (1) evaluate drivers of post-fire changes in turbidity and stream temperature; (2) examine mechanisms and processes responsible for spatial and temporal variabilities of changes; and (3) address scale-dependent knowledge gaps to recommend future research directions. Positive correlations between turbidity changes following wildfire were heavily influenced by fire severity, forest diversity, and landscape alterations by human activities such as salvage logging. Stream temperature increases result from loss of riparian canopy cover and decreased shading, but they were highly site-specific and dependent on topographic variations. We attribute variabilities in our findings to climate variability and heavy disparity across spatial and temporal scales when assessing the direction and magnitude of post-fire impacts. Future research should incorporate more long-term rigorous monitoring efforts and spatiotemporally explicit models to better represent the complex post-fire hydrologic system that influences water quality.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET′) reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

Efficiency of a multi-barrier household system for surface water treatment combining a household slow sand filter to a Mesita Azul® ultraviolet disinfection device

Low-cost household technologies for water treatment are crucial to improving drinking water quality and preventing health, social and economic impacts, mostly in middle- and low-income regions. This work assessed the removal efficiency of physical-chemical and bacteriological parameters from river water by a multi-barrier household water treatment system for 113 consecutive days. This system combines a pre-treatment step through a non-woven synthetic blanket, filtration by an intermittent household slow sand filter (HSSF) and a Mesita Azul® ultraviolet disinfection device. In general, the water quality was improved by the evaluated system. Turbidity was removed by an average of 73% (ranging from 33 to 94%), total coliforms (TC) of 3.88 log10 (ranging from 2.22 to 5.16 log10) and E. coli of 2.49 log10 (ranging from 1.81 to 3.30 log10). Filtration improvement was mostly correlated to HSSF biofilm development and influent water quality. Characterisation of HSSF schmutzdecke demonstrated a predominance of organic content, and a higher presence of carbohydrates than proteins on the sand and the blanket. Ultraviolet disinfection with Mesita Azul® inactivated most of the remaining bacteria after filtration and no regrowth was observed after 15 days of disinfection. In conclusion, the multi-barrier household water treatment system was efficient in treating river water, reducing risks of microbial contamination to achieve safe drinking water.

Spatial Variation in the Composition and Diversity of Fishes Inhabiting an Artificial Water Supply Lake, Eastern China

With the intensification of eutrophication, many artificial water supply lakes that act as a biological filter for water diverted from rivers have been built to alleviate water scarcity in Eastern China. In this study, we selected Lake Yanlong, a representative artificial water supply lake in Yancheng City, as our experimental lake to explore how the community composition of fishes changed among different habitat types and assess potential consequences for effective water treatment. From October 2015 to October 2020, we conducted quarterly surveys of fish communities and environmental factors in the Mangshe River (MR; water for lake) compared to four different water treatment zones of Lake Yanlong (PZ, the pretreatment zone with inlet from the MR; EZ, the emergent macrophyte zone; SZ, the submerged macrophyte zone; DZ, the deep purification zone with outlet to urban waterworks). A total of 16,339 individual fish belonging to 11 families and 49 species were collected. Six of the eight dominant species observed across zones were small-bodied fishes. Despite reduced fish species richness, the relative abundance and biomass of fishes tended to be higher in Lake Yanlong relative to the MR. The Shannon-Wiener diversity index, Pielou evenness index, Simpson’s diversity index all decreased from the MR to the DZ in the following sequence: MR < PZ < EZ < SZ < DZ. Analysis of similarities and similarity percentage analysis confirmed that fish communities differed significantly among zones and Coilia ectenes, Carassius auratus, Pseudobrama simony, Hemiculter leucisculus, and Hemiculter bleekeri were the major differentiating species. Mantel’s test and redundancy analysis revealed that water depth, aquatic vegetation coverage, and phytoplankton concentrations were the major factors determining the spatial distribution of fishes when moving from the MR to the DZ of Lake Yanlong. Stocking piscivorous fish can be used as an effective measure to control the abundance of prolific small-bodied fishes in Lake Yanlong. The details backing these findings are important for understanding how the community composition of fishes among habitat types in Lake Yanlong influence water quality, and to develop suitable biomanipulation strategies for the management of fish resources and maintaining proper function of these artificial water supply lakes.

Assessing the long-term evolution of mine water quality in abandoned underground mine workings using first-flush based models

Coal mining activities can leave an extensive network of abandoned underground workings that gradually flood after operations cease. This rising mine water can eventually lead to uncontrolled releases of harmful acid mine drainage (AMD) to the environment. Treatment plants are used to extract and treat the mine water to maintain its elevation below suspected discharge points. Accurate predictions of long-term water quality, and treatment plant operations, are highly challenging due to the complexity and volume of the underground workings. As numerical models require considerable effort to effectively implement, empirical models that are based on the 'first-flush' phenomenon, where mine water concentrations peak shortly after flooding and then exponentially decline, may provide suitable long-term predictions. The objective of this study was to assess the robustness of first-flush based models for describing mine water behavior at large, complex mine pools in the Sydney Coalfield (Nova Scotia, Canada). Numerous mine pools across the coalfield flooded at various times over 100+ years, with extensive mine water quality data available in various pools of different ages. The historical evolution of mine water quality demonstrated first-flush behavior across key AMD indicator parameters (acidity, sulfate, iron), concentration ranges, and mine pool depths. Two ‘newer’ mine pools, which only flooded in the past 10–15 years, rely on an active treatment plant to manage mine water levels below environmental discharge points. Influent water quality from each mine pool was sampled bi-weekly between 2011 and 2022, and first-flush models were then applied to predict the future quality of mine water entering the treatment plant over the long-term. Knowledge on long-term influent quality can help to optimize treatment plant requirements and related expenses.

Performance Comparison of EGSB and IC Reactors for Treating High-Salt Fatty Acid Organic Production Wastewater

This study used the EGSB and IC reactors to treat the high-salt and high-concentration organic wastewater (high-salt fatty acid production wastewater) and compared their performances The experimental results showed that the optimal influent water quality thresholds for both bioreactors to treat this wastewater were a COD concentration of 18,000 mg/L and a sulfate ion concentration of about 8000 mg/L. The reactor operated well when C/S was greater than 2.8. In addition, the value of C/S should not be less than 1.5. This is due to that under this condition, the sulfate reduction process has a significant impact on the removal of COD, and MPB may be inhibited by sulfides. The organic load OLR should not be greater than 10 kgCOD/(m3·d). It was also found that the start-up time of the IC reactor with external circulation was slightly shorter, and the COD removal effect, gas production rate, and load tolerance were slightly better than those of the EGSB reactor, the best reflux ratio of the two reactors was 6:1. The appropriate rising flow rate was 0.4 m/h.

A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

In this investigation, we have studied and designed a type-2 fuzzy logic controller (IT2FLC) for the wastewater treatment plant at Haldia, India. To avoid modeling complex physical, chemical, and biological treatment processes of wastewater, this present work represents an ensemble of fuzzy models as surrogates for the wastewater treatment plant (WWTP). Using measured influent water quality data, each fuzzy model predicts water quality after the process of water treatment parameters. The pH, biological oxygen demand (BOD), total suspended particles (TSS), chemical oxygen demand (COD), and temperature are taken into account as input variables. Finally, the sensitivity of the IT2FLC model is evaluated by several statistical parameters like RMSE, MAE, MAPE, and most importantly R 2 value. For the current model, the values of the three parameters are almost 0, whereas the value of the R 2 is almost close to 1, which signifies that the IT2FLC model is accurate and more efficient in predicting response compared to other conventional methods reported in various literatures.

Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning.

Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.